Search Results (62)

Infrared (IR) spectroscopy is a powerful tool for characterizing molecular structures and chemical groups, offering advantages such as low cost, rapid analysis, and non-destructive testing. When analyzing heterogeneous objects, spectra are typically measured from different regions to capture the local variations, presenting a multi-instance learning (MIL) problem. However, existing methods primarily rely on multi-instance assumptions or explicit bag representations, often failing to fully capture the intrinsic information from implicit representations. We introduce a bag dissimilarity regularized MIL framework for analyzing IR spectra of heterogeneous objects, which integrates both explicit and implicit representations to effectively learn the MIL bags. Specifically, a bag dissimilarity regularization term is utilized to extract implicit representations, which subsequently guide the classifier based on explicit representations to enhance generalization performance. The proposed method was validated on two heterogeneous detection tasks: polydimethylsiloxane (PDMS) block assessment and polyethylene terephthalate (PET) fiber inspection. Experimental results demonstrate that our approach significantly outperforms existing methods on both datasets with a considerable margin. Full article

The rapid growth of Internet of Medical Things (IoMT) devices has revolutionized diagnostics and patient care within smart healthcare networks. However, this progress has also expanded the attack surface due to the heterogeneity and interconnectivity of medical devices. To overcome the limitations of traditional batch-trained security models, this study proposes an adaptive online intrusion detection framework designed for real-time operation in dynamic healthcare environments. The system combines Leveraging Bagging with Hoeffding Tree classifiers for incremental learning while integrating the Page–Hinkley test to detect and adapt to concept drift in evolving attack patterns. A modular and scalable network architecture supports centralized monitoring and ensures seamless interoperability across various IoMT protocols. Implemented within a low-latency, high-throughput stream-processing pipeline, the framework meets the stringent clinical requirements for responsiveness and reliability. To simulate streaming conditions, we evaluated the model using the CICIoMT2024 dataset, presenting one instance at a time in random order to reflect dynamic, real-time traffic in IoMT networks. Experimental results demonstrate exceptional performance, achieving accuracies of 0.9963 for binary classification, 0.9949 for six-class detection, and 0.9860 for nineteen-class categorization. These results underscore the framework’s practical efficacy in protecting modern healthcare infrastructures from evolving cyber threats. Full article

Background/Objectives: Arboviral diseases share common vectors, geographic distribution, and symptoms. Developing Machine Learning diagnostic tools for co-circulating arboviral diseases faces data-scarcity challenges. This study aimed to demonstrate that proof of concept using synthetic data can establish computational feasibility and guide future real-world validation efforts. Methods: We assembled a synthetic dataset of 28,000 records, with 7000 for each disease—Dengue, Zika, and Chikungunya—plus Influenza as a negative control. These records were obtained from the existing literature. A binary matrix with 67 symptoms was created for detailed statistical analysis using Odds Ratios, Chi-Square, and symptom-specific conditional prevalence to validate the clinical relevance of the simulated data. This dataset was used to train and evaluate various algorithms, including Multi-Layer Perceptron (MLP), Narrow Neural Network (NN), Quadratic Support Vector Machine (QSVM), and Bagged Tree (BT), employing multiple performance metrics: accuracy, precision, sensitivity, specificity, F1-score, AUC-ROC, and Cohen’s kappa coefficient. Results: The dataset aligns with the PAHO guidelines. Similar findings are observed in other arboviral databases, confirming the validity of the synthetic dataset. A notable performance across all evaluated metrics was observed. The NN model achieved an overall accuracy of 0.92 and an AUC above 0.98, with precision, sensitivity, and specificity values exceeding 0.85, and an average Uniform Cohen’s Kappa of 0.89, highlighting its ability to reliably distinguish between Dengue and Influenza, with a slight decrease between Zika and Chikungunya. Conclusions: These models could accelerate early diagnosis of arboviral diseases by leveraging encoded symptom features for Machine Learning and Deep Learning approaches, serving as a support tool in regions with limited healthcare access without replacing clinical medical expertise. Full article

Debit and credit card payments have become the preferred method of payment for consumers, replacing paper checks and cash. However, this shift has also led to an increase in concerns regarding identity theft and payment security. To address these challenges, it is crucial to develop an effective, secure, and reliable payment system. This research presents a comprehensive study on payment card fraud detection using deep learning techniques. The introduction highlights the significance of a strong financial system supported by a quick and secure payment system. It emphasizes the need for advanced methods to detect fraudulent activities in card transactions. The proposed methodology focuses on the conversion of a comma-separated values (CSV) dataset into quick response (QR) code images, enabling the application of deep neural networks and transfer learning. This representation enables leveraging pre-trained image-based architectures by encoding numeric transaction attributes into visual patterns suitable for convolutional neural networks. The feature extraction process involves the use of a convolutional neural network, specifically a residual network architecture. The results obtained through the under-sampling dataset balancing method revealed promising performance in terms of precision, accuracy, recall, and F1 score for the traditional models such as K-nearest neighbors (KNN), Decision Tree, Random Forest, AdaBoost, Bagging, and Gaussian Naïve Bayes. Furthermore, the proposed deep neural network model achieved high precision, indicating its effectiveness in detecting card fraud. The model also achieved high accuracy, recall, and F1 score, showcasing its superior performance compared to traditional machine learning models. In summary, this research contributes to the field of payment card fraud detection by leveraging deep learning techniques. The proposed methodology offers a sophisticated approach to detecting fraudulent activities in card payment systems, addressing the growing concerns of identity theft and payment security. By deploying the trained model in an Android application, real-time fraud detection becomes possible, further enhancing the security of card transactions. The findings of this study provide insights and avenues for future advancements in the field of payment card fraud detection. Full article

Background and Objective: Prostate cancer remains one of the most prevalent and potentially lethal malignancies among men worldwide, and timely and accurate diagnosis, along with the stratification of patients by disease severity, is critical for personalized treatment and improved outcomes for this cancer. One of the tools used for diagnosis is bioinformatics. However, traditional biomarker discovery methods often lack transparency and interpretability, which means that clinicians find it difficult to trust biomarkers for their application in a clinical setting. Methods: This paper introduces a novel approach that leverages Explainable Machine Learning (XML) techniques to identify and prioritize biomarkers associated with different levels of severity of prostate cancer. The proposed XML approach presented in this study incorporates some traditional machine learning (ML) algorithms with transparent models to facilitate understanding of the importance of the characteristics for bioinformatics analysis, allowing for more informed clinical decisions. The proposed method contains the implementation of several ML classifiers, such as Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), and Bagging (Bg); followed by SHAPly values for the XML pipeline. In this study, for pre-processing of missing values, imputation was applied; SMOTE (Synthetic Minority Oversampling Technique) and the Tomek link method were applied to handle the class imbalance problem. The k-fold stratified validation of machine learning (ML) models and SHAP values (SHapley Additive explanations) were used for explainability. Results: This study utilized a novel tissue microarray data set that has 102 patient data comprising prostate cancer and healthy patients. The proposed model satisfactorily identifies genes as biomarkers, with highest accuracy obtained being 81.01% using RF. The top 10 potential biomarkers identified in this study are DEGS1, HPN, ERG, CFD, TMPRSS2, PDLIM5, XBP1, AJAP1, NPM1 and C7. Conclusions: As XML continues to unravel the complexities within prostate cancer datasets, the identification of severity-specific biomarkers is poised at the forefront of precision oncology. This integration paves the way for targeted interventions, improving patient outcomes, and heralding a new era of individualized care in the fight against prostate cancer. Full article

As cyber threats such as denial-of-service (DoS) attacks continue to rise, network intrusion detection systems (NIDS) have become essential components of cybersecurity defense. Although machine learning is widely applied to network intrusion detection, its performance often deteriorates due to the extreme class imbalance present in real-world data. This imbalance causes models to become biased and unable to detect critical attack instances. To address this issue, we propose MCH-Ensemble (Minority Class Highlighting Ensemble), an ensemble framework designed to improve the detection of minority attack classes. The method constructs multiple balanced subsets through random under-sampling and trains base learners, including decision tree, XGBoost, and LightGBM models. Features of correctly predicted attack samples are then amplified by adding a constant value, producing a boosting-like effect that enhances minority class representation. The highlighted subsets are subsequently combined to train a random forest meta-model, which leverages bagging to capture diverse and fine-grained decision boundaries. Experimental evaluations on the UNSW-NB15, CIC-IDS2017, and WSN-DS datasets demonstrate that MCH-Ensemble effectively mitigates class imbalance and achieves superior recognition of DoS attacks. The proposed method achieves enhanced performance compared with those reported previously. On the UNSW-NB15 and CIC-IDS2017 datasets, it achieves improvements in accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) by ~1.2% and ~0.61%, ~9.8% and 0.77%, ~0.7% and ~0.56%, ~5.3% and 0.66%, and ~0.1% and ~0.06%, respectively. In addition, it achieves these improvements by ~0.17%, ~1.66%, ~0.11%, ~0.88%, and ~0.06%, respectively, on the WSN-DS dataset. These findings indicate that the proposed framework offers a robust and accurate approach to intrusion detection, contributing to the development of reliable cybersecurity systems in highly imbalanced network environments. Full article

Within the rapidly advancing disciplines of natural language processing (NLP) and artificial intelligence (AI), this paper introduces an innovative approach aimed at improving access to health-related information. Fueled by the growing reliance on digital platforms for health inquiries, our research unveils an intelligent chatbot designed to categorize health-related queries and deliver personalized advice. By leveraging a diverse dataset and employing advanced NLP techniques, our models, which include Support Vector Machines, Random Forest, Bagging Classifier, among others, assist in building a flexible conversational agent. The evaluation metrics demonstrate that the Bagging Classifier delivers outstanding results, reaching an accuracy of 99%. The study concludes with a comparative analysis, positioning the Bagging Classifier as a benchmark for accuracy and performance in the classification of health-related queries. Full article

As a fundamental information carrier in Industrial Internet of Things (IIoT), electromagnetic spectrum data presents critical challenges for efficient spectrum sensing and situational awareness in smart industrial cognitive radio systems. Addressing sparse sampling limitations caused by energy-constrained transceiver nodes in Unmanned Aerial Vehicle (UAV) spectrum monitoring, this paper proposes a compressive sensing-based 3D spectrum tensor completion framework for extrapolative reconstruction in obstructed areas (e.g., building occlusions). First, a Sparse Coding Neural Gas (SCNG) algorithm constructs an overcomplete dictionary adaptive to wide-range spectral fluctuations. Subsequently, a Bag of Pursuits-optimized Orthogonal Matching Pursuit (BoP-OOMP) framework enables adaptive key-point sampling through multi-path tree search and temporary orthogonal matrix dimensionality reduction. Finally, a Neural Gas competitive learning strategy leverages intermediate BoP solutions for gradient-weighted dictionary updates, eliminating computational redundancy. Benchmark results demonstrate 43.2% reconstruction error reduction at sampling ratios r ≤ 20% across full-space measurements, while achieving decoupling of highly correlated overlapping subspaces—validating superior estimation accuracy and computational efficiency. Full article

Artificial intelligence has become crucial in sports, leveraging its analytical capabilities to enhance the understanding and prediction of complex events. Machine learning algorithms in sports, especially basketball, are transforming performance analysis by identifying patterns and trends invisible to traditional methods. This technology provides in-depth insights into individual and team performance, enabling precise evaluation of strategies and tactics. Consequently, the detailed analysis of every aspect of a team’s routine can significantly elevate the level of competition in the sport. This study investigates a range of machine learning models, including Logistic Regression (LR), Ridge Regression Classifier (RR), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Stacking Classifier (STACK), Bagging Classifier (BAG), Multi-Layer Perceptron (MLP), AdaBoost (AB), and XGBoost (XGB), as well as deep learning architectures such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to compare their effectiveness in predicting game outcomes in the NBA and WNBA leagues. The results show highly acceptable prediction accuracies of 65.50% for the NBA and 67.48% for the WNBA. This study allows us to understand the impact that artificial intelligence can have on the world of basketball and its current state in relation to previous studies. It can provide valuable insights for coaches, performance analysts, team managers, and sports strategists by using machine learning and deep learning models to predict NBA and WNBA outcomes, enabling informed decisions and enhancing competitive performance. Full article

The spotted lanternfly, Lycorma delicatula (Hemiptera: Fulgoridae) (SLF), is a damaging invasive pest and generalist phloem feeder that has been found in 18 states in the United States. It has a complex multimodal communication system involving semiochemicals, emitted both from their honeydew and their bodies, and substrate-borne vibrations. Sensitive and effective traps for detection and survey are essential management tools, but no potent lures for SLF exist yet. We sought to test an alternative that relies on live-trapped SLF acting as lures to improve trap efficacy after the first SLF is captured. SLF circle traps were modified by replacing the commonly used plastic collection bag with a mesh bag pinned to the tree trunk. These allowed the trapped SLF to remain alive and generate signals through the mesh bag, thus leveraging their natural modes of communication to draw additional SLF into the traps. We compared mesh and plastic bags over three years targeting fourth instars and adults and found that prior to oviposition, circle traps with mesh bags captured significantly more fourth instar (70% mesh: 30% plastic) and adult SLF (59% mesh: 41% plastic) compared to plastic bags, but during oviposition time, the results were mixed. Full article

The increasing demand for energy efficiency in manufacturing has driven the need for advanced modeling techniques to optimize power consumption in machining processes. This study presents a novel approach to modeling power consumption in milling processes using machine learning, leveraging a custom-designed braking device integrated into the milling machine’s main spindle to measure friction forces with high precision. A comprehensive dataset of observations, including parameters such as speed, force, intensity, apparent power, active power, and power factor, was collected under loaded conditions. Nine machine learning models—Linear Regression, Random Forest, Support Vector Regression, Polynomial Regression, Multi-Layer Perceptron with 2 and 3 layers, K-Nearest Neighbors, Bagging, and a hybrid Probabilistic Random Forest—Multi-Layer Perceptron (PIRF–MLP)—were evaluated using 5-fold cross-validation to ensure robust performance assessment. The PIRF–MLP model achieved the highest performance, demonstrating superior accuracy in predicting utile power. The feature importance analysis revealed that force and speed significantly influence power consumption. The proposed methodology, validated on a milling machine, offers a scalable solution for real-time energy monitoring and optimization in machining, contributing to sustainable manufacturing practices. Future work will focus on expanding the dataset and testing the models across diverse machining conditions to enhance generalizability. Full article

Background/Objectives: Diabetic foot ulcers (DFUs) remain a critical complication of diabetes, with high rates of amputation when not diagnosed early. Despite advancements in medical imaging, current DFU detection methods are often limited by their computational complexity, poor generalizability, and delayed diagnostic performance. This study presents a novel hybrid diagnostic framework that integrates traditional feature extraction methods with deep learning (DL) to improve the early real-time computer-aided detection (CAD) of DFUs. Methods: The proposed model leverages plantar thermograms to detect early thermal asymmetries associated with DFUs. It uniquely combines the oriented FAST and rotated BRIEF (ORB) algorithm with the Bag of Features (BOF) method to extract robust handcrafted features while also incorporating deep features from pretrained convolutional neural networks (ResNet50, AlexNet, and EfficientNet). These features were fused and input into a lightweight deep neural network (DNN) classifier designed for binary classification. Results: Our model demonstrated an accuracy of 98.51%, precision of 100%, sensitivity of 98.98%, and AUC of 1.00 in a publicly available plantar thermogram dataset (n = 1670 images). An ablation study confirmed the superiority of ORB + DL fusion over standalone approaches. Unlike previous DFU detection models that rely solely on either handcrafted or deep features, our study presents the first lightweight hybrid framework that integrates ORB-based descriptors with deep CNN representations (e.g., ResNet50 and EfficientNet). Compared with recent state-of-the-art models, such as DFU_VIRNet and DFU_QUTNet, our approach achieved a higher diagnostic performance (accuracy = 98.51%, AUC = 1.00) while maintaining real-time capability and a lower computational overhead, making it highly suitable for clinical deployment. Conclusions: This study proposes the first integration of ORB-based handcrafted features with deep neural representations for DFU detection from thermal images. The model delivers high accuracy, robustness to noise, and real-time capabilities, outperforming existing state-of-the-art approaches and demonstrating strong potential for clinical deployment. Full article

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

Monitoring Peripheral Oxygen Saturation (SpO₂) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO₂-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO₂ without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO₂-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD $\pm 1.96\sigma$ = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO₂ estimation. Full article

Cardiovascular diseases (CVDs) rank among the leading global causes of mortality, underscoring the necessity for early detection and effective management. This research presents a novel prediction model for CVDs utilizing a bagging algorithm that incorporates histogram gradient boosting as the estimator. This study leverages three preprocessed cardiovascular datasets, employing the Local Outlier Factor technique for outlier removal and the information gain method for feature selection. Through rigorous experimentation, the proposed model demonstrates superior performance compared to conventional machine learning approaches, such as Logistic Regression, Support Vector Classification, Gaussian Naïve Bayes, Multi-Layer Perceptron, k-nearest neighbors, Random Forest, AdaBoost, gradient boosting, and histogram gradient boosting. Evaluation metrics, including precision, recall, F1 score, accuracy, and AUC, yielded impressive results: 93.90%, 98.83%, 96.30%, 96.25%, and 0.9916 for dataset I; 94.17%, 99.05%, 96.54%, 96.48%, and 0.9931 for dataset II; and 89.81%, 82.40%, 85.91%, 86.66%, and 0.9274 for dataset III. The findings indicate that the proposed prediction model has the potential to facilitate early CVD detection, thereby enhancing preventive strategies and improving patient outcomes. Full article